The book “Digital Transformation in Banking Industry – Financial Security, Audit and Business Perspective” explores the effect of digital transformation on Anti-Money Laundering (AML) regime by focusing on digital Know Your Client (eKYC), artificial intelligence in AML, crypto currency, and virtual assets by drawing attention on ongoing compliance challenges in “virtual assets era”. Furthermore, it explores Fintechs and future of conventional banking as well as remote auditing in banking industry.

This edition explores Fintechs and the future of conventional banking. This is to examine the Fintech structures that have recently gained great importance in the world and to evaluate the possible effects of the developments on traditional banking in the future. Considering the innovations that Fintech structures bring to payment systems, their impact on credit markets and the structure of blockchain-linked contracts, it is expected that they will have greater effects on the financial system in the future. In this context, the book analysis the effects of these structures on traditional banking by including the recent literature on the subject.

Remote auditing is one of the common methods that is suitable for today’s world with its flexible and technology-based structure. In our book we have dedicated a chapter which explores remote auditing (also known as virtual audit) and challenges as well as advantages of applying it.

A chapter is dedicated to AML Programs in Banking Industry in Covid-19 Era, which identifies challenges, good practices, and policy reactions to new ML/TF risks which can be also defined as financial security risks arising during the Covid-19 pandemic crisis and due to impact of digital transformation in banking industry.

A special attention is dedicated to the artificial intelligence in AML structures in banking and the role of artificial intelligence for detecting money laundering cases by decreasing the number of “false alerts” and increasing the quality of “real matches”. The book explores the role of artificial intelligence on increasing the efficiency and decreasing the manual work on daily basis for AML Compliance Officers.

The book explores virtual currency and blockchain by identifying ongoing compliance challenges from decentralized platforms, peer-to-peer cryptocurrency exchangers, anonymity-enhanced cryptocurrencies, and “jurisdictional arbitrage” (the ability of criminal actors to route their activity through countries with weak AML/CFT regimes). Blockchain/ Distributed Ledger Technology (DLT) applications aside, certain properties of Virtual Currencies (including VCs associated with Initial Coin Offering – ICOs) entail special Financial Crimes Compliance (FCC) risks, some of which are detailed in our book.

Nowadays the Digital Identity (DI) services help financial institutions, especially banking industry, to offer several services efficiently and properly to their clients. By keeping this in mind, the measures for Covid-19, offering services in distance remains the best solution in the market. The DI provides provable and sharable identity, which is particularly important when on-boarding clients but also has a key protection role to play when users are transacting. In our book we have dedicated a chapter related to digital ID system and eKYC.

We believe this edition contributes to current literature for the effect of digital transformation in banking industry from the financial security, audit and business perspective and it is important for compliance officers and bankers, policy makers, strategists, academics, students, and professionals in the field of compliance.

Financial time series including high frequency structures like jumps, spikes and stochastic volatility are usually modeled in an ad-hoc manner by stochastic differential equations together with Levy processes. Estimating the parameters and determining the jump size distributions do not have a precise and universally accepted method. Under these circumstances, complexities and confusions usually arise. This book, approaches to this issue from a very different angle through introducing an autocorrelation one process together with finite number of Fourier series terms. Introduction of Fourier series to estimate the dynamics of the process is not done in an ad-hoc manner or as done before in dealing with seasonality. Here the moving average is transformed to a “moving and fluctuating” average by the help of Fourier series. Instead of adding jump structures to the model which makes the parameter estimation quite cumbersome, our model in discrete time can easily be transformed to a well-known mean reverting continuous time process. Moreover, our alternative model turned out to be a quite powerful and accurate forecasting technique.